High-content screening (“HCS”) has been developed to address the need for more detailed information about the temporal-spatial dynamics of cell constituents and processes, and plays an important role in the use of cell-based screening for identification and validation of drug candidates. High-content screens automate the extraction of fluorescence information derived from specific fluorescence-based reagents incorporated into cells attached to a substrate (Giuliano and Taylor (1995), Curr. Op. Cell Biol. 7:4; Giuliano et al. (1995) Ann. Rev. Biophys. Biomol. Struct. 24:405). Cells are analyzed using an imaging system that can measure spatial as well as temporal dynamics. (Farkas et al. (1993) Ann. Rev. Physiol. 55:785; Giuliano et al. (1990) In Optical Microscopy for Biology. B. Herman and K. Jacobson (eds.), pp. 543-557. Wiley-Liss, New York; Hahn et al (1992) Nature 359:736; Waggoner et al. (1996) Hum. Pathol. 27:494). The concept is to treat each cell as a “well” that has spatial and temporal information on the activities of the labeled constituents.
High-content screens can be performed on either fixed cells, using fluorescently labeled antibodies, biological ligands, and/or nucleic acid hybridization probes, or live cells using multicolor fluorescent indicators and “biosensors.” The choice of fixed or live cell screens depends on the specific cell-based assay required. The types of biochemical and molecular information now accessible through fluorescence-based reagents applied to cells include ion concentrations, membrane potential, specific translocations, enzyme activities, gene expression, as well as the presence, amounts and patterns of metabolites, proteins, lipids, carbohydrates, and nucleic acid sequences (WO 98/38490; DeBiasio et al., (1996) Mol. Biol. Cell. 7:1259; Giuliano et al., (1995) Ann. Rev. Biophys. Biomol. Struct. 24:405; Heim and Tsien, (1996) Curr. Biol. 6:178).
It is important that local differences in the imaging system and software associated with the optics, illumination, geometry of the plate, or other assay-specific parameters, be minimized to ensure reproducibility and value of the information derived from performing HCS.
Currently, there are no tools designed for diagnostics, calibration, or software validation of fluorescence imaging systems that carry out image-based microscopic measurements and analysis. Such a tool is valuable for HCS assays performed on physically attached cells or objects, as well as for general biological research microscopes, defect identification imaging systems (such as polarization microscopes), industrial or commercial particle counting imaging systems (such as particle counters for explosives detection, and spore and pollen detection.
Previous calibration tools have generally been adapted for use with flow cytometers, such as those using suspensions of fluorescent microbeads to calibrate the illumination, alignment, optics and fluidics of the flow cytometer. Other methods have involved the use of uniform fluorescent films. However, such methods only provide information about fluorescent intensity, and provide no information about size, shape, or spatial distribution of the fluorescent signal, and thus do not permit calibration of an imaging system for these types of parameters.
Thus, the existence of a tool that contains spatial information for diagnostics, calibration, or software validation for verifying image analysis and integrated system accuracy and reproducibility is needed in the art. Instrument calibration is especially important in live cell applications, since slight differences in illumination can have a tremendous impact in the performance of the assay, due to phototoxicity and other issues. An easy-to-use tool for diagnostics, calibration, or software validation would also allow instrument testing prior to each run with automated protocols, and normalization for variability in hardware or software.
A tool in which fluorescent microbeads are bound to a surface would allow absolute reproducibility regarding the actual objects being imaged, such as size, shape, and spatial distribution, as well as the exact position on the test plate on which the objects are imaged (X, Y and Z coordinates). Such a tool would be useful for 1) calibrating imaging systems by measuring known input parameters and adjusting the system to normalize or rescale resulting output data; 2) diagnosing whether system operation is within specifications and to solve problems if performance is out of specifications by testing system sub-components; and 3) testing integrated system performance by determining the veracity of output with known input.